Bayesian representation learning in the cortex regulated by acetylcholine
Bayesian representation learning in the cortex regulated by acetylcholine
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DOI：
10.1016/j.neunet.2004.06.006
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发表时间：
2004-12-01
期刊：
影响因子：
7.8
通讯作者：
Ishii, S
中科院分区：
文献类型：
--
作者：
Hirayama, J;Yoshimoto, J;Ishii, S
大脑需要检测环境变化，并快速学习新环境中必要的内部表征。本文结合以往对乙酰胆碱（ACh）功能作用的研究结果，提出了一个适应动态环境的皮层表征学习理论模型。本文采用概率主成分分析（PPCA）作为皮质表征学习的功能模型，提出了一种基于贝叶斯推理的PPCA在线学习方法，并给出了模型选择的启发式准则。我们的方法在合成和现实数据集的两种模拟中进行了检验，其中我们的模型能够在环境变化后重新学习新的表示库。我们的模型表明，较高水平的识别可能会调节较低水平的皮质乙酰胆碱释放，并且乙酰胆碱水平会改变局部电路的学习动态，以便在动态环境中不断获得适当的表征。(C) 2004 Elsevier Ltd.版权所有。
A brain needs to detect an environmental change and to quickly learn internal representations necessary in a new environment. This paper presents a theoretical model of cortical representation learning that can adapt to dynamic environments, incorporating the results by previous studies on the functional role of acetylcholine (ACh). We adopt the probabilistic principal component analysis (PPCA) as a functional model of cortical representation learning, and present an on-line learning method for PPCA according to Bayesian inference, including a heuristic criterion for model selection. Our approach is examined in two types of simulations with synthesized and realistic data sets, in which our model is able to re-learn new representation bases after the environment changes. Our model implies the possibility that a higher-level recognition regulates the cortical ACh release in the lower-level, and that the ACh level alters the learning dynamics of a local circuit in order to continuously acquire appropriate representations in a dynamic environment. (C) 2004 Elsevier Ltd. All rights reserved.